RÉSUMÉ : The production routing problem (PRP) concerns the production and distribution of a single product from a production plant to multiple customers using capacitated vehicles in a discrete and finite time horizon. The PRP is a generalization of the inventory routing problem (IRP) obtained by incorporating production lot-sizing decisions. In this talk, we discuss the stochastic PRP under demand uncertainty in a two-stage decision process. The first stage consists of making setup and routing decisions before the realization of demand, and the second stage involves quantity decisions made when the demand becomes known. We develop exact solution approaches based on Benders decomposition to solve the problem. Two different Benders reformulation schemes together with several enhancements are proposed and they are implemented within a branch-and-cut framework. This implementation is called branch-and-Benders-cut (BBC) and the computational experiments show that it outperforms the standard implementation of the Benders algorithm. The BBC also provides superior results to the branch-and-cut approach, which is the current best exact algorithm for the deterministic PRP, when solving a large number of scenarios. We further discuss the reoptimization capabilities of the Benders approach which can be particularly useful in two stochastic environments, namely, a sample average approximation scheme (SAA) to handle a large number of scenarios, and a rolling horizon framework (RH) for a dynamic and stochastic variant of the PRP.